Metrics

in silico mixture results

Perform benchmarking on in silico mixture results.

Contains code to regenerate figures 2b-d, 3, 4 and supplementary figures 8-9 from the SVclone paper.

Figure 2b

Optimal versus ground truth CCF distributions. From left to right, by column: i) SV theoretical mixture truth CCFs, ii) ‘optimal’ SV CCFs based on sample membership, iii) SNV theoretical mixture truth CCF, iv) ‘optimal’ SNV CCFs based on sample membership.

This figure is an extended version of the figure that appears in the paper.

CCF mean error

##    data          V1
## 1:  SVs 0.092590175
## 2: SNVs 0.002067377

Figure 2c

True minus optimal CCF averages for SNVs (red) and SVs (blue).

Figure 2d

ROC curves showing sensitivity and specificity of classifying variants as subclonal:

• red: SNVs
• gold: SV max
• green: SV mean
• purple: SV min

Respective AUCs

## SV_ccf_mean  SV_ccf_min  SV_ccf_max     SNV_ccf 
##   0.9083199   0.8862681   0.9161043   0.9039557

SV CCF mean optimal cutoff

## [1] 0.6910936

SV CCF min optimal cutoff

## [1] 0.6515464

SV CCF min optimal cutoff

## [1] 0.7796553

SNV CCF optimal cutoff

## [1] 0.7182253

Figure 3

Performance for SVs that have clonal background copy-numbers vs. all SVs (clonal + subclonal background copy-numbers).

Figure 4

Performance across SVclone (SVs and SNVs), battenberg and PyClone.

Supplementary Figure 8

Performance of post-assignment using a joint SV + SNV model, compared with SV and SNV results wihout post-assign. PyClone results added for reference.

Supplementary Figure 9

PyClone precision traces.